Võrdle meetodeid
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| Heterogeneous Treatment Effect Event Study Design× | Erinevused erinevustes (Diff-in-Diff)× | |
|---|---|---|
| Valdkond≠ | Põhjuslik järeldamine | Ökonomeetria |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 2021 | 1994 |
| Looja≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Tüüp≠ | Quasi-experimental causal inference | Causal inference / panel regression |
| Algallikas≠ | Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Rööpnimetused≠ | HTE event study, heterogeneous effects event study, group-time ATT event study, dynamic HTE design | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Seotud≠ | 3 | 5 |
| Kokkuvõte≠ | Heterogeneous Treatment Effect Event Study Design is a causal-inference framework that uses event study regression to estimate how treatment effects vary across groups, cohorts, or time relative to a treatment event. Unlike classical two-way fixed-effects event studies — which assume a homogeneous effect — this approach explicitly models and recovers group-time average treatment effects (ATTs), addressing the contamination bias that arises when effects differ across treated units. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
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